Get group model fit for a specific model

Fits a model to all available datasets and returns the group-level fit. This function provides a convenient way to get pre-computed model fits for common models.

Usage

get_group_model_fit(
  model_name,
  datasets = NULL,
  control = xslControl(),
  deoptim_control = DEoptim::DEoptim.control(reltol = 0.001, NP = 100, itermax = 100),
  n_restarts = NULL,
  seed = NULL,
  fit_lapse = FALSE
)

Arguments

model_name

Name of the model to fit (see show_models())

datasets

Optional list of datasets to fit to (defaults to all available)

control

Control arguments passed to xsl_run() (via xsl_fit()); notably n_sim, the number of simulations averaged per evaluation of a stochastic model (default 500 – lowering this can speed up fitting of stochastic models substantially).

deoptim_control

Control parameters passed to DEoptim() (via xsl_fit()).

n_restarts

Number of DEoptim runs to keep the best of; defaults to the model's registry default (5 for uncfam_predictive, 3 for tilles, otherwise 1).

seed

Optional random seed for reproducible fits.

fit_lapse

With a likelihood objective (control = xslControl(objective = "binomial") or "multinomial"), also fit the lapse rate, in [0, 1].

Value

A list containing the fitted model results

Examples

# Get group fit for the uncfam model. A small dataset subset and a
# reduced DEoptim search keep this example fast; drop those arguments
# for a real, thorough fit.
group_fit <- get_group_model_fit(
  "uncfam", datasets = xsl_datasets[1:3],
  deoptim_control = DEoptim::DEoptim.control(NP = 10, itermax = 5))
#> Warning: For many problems it is best to set 'NP' (in 'control') to be at least ten times the length of the parameter vector. 
#> Iteration: 1 bestvalit: 5.155958 bestmemit:    0.572436    4.886378    0.964311
#> Iteration: 2 bestvalit: 5.155958 bestmemit:    0.572436    4.886378    0.964311
#> Iteration: 3 bestvalit: 4.683360 bestmemit:    1.070998    3.482776    0.990162
#> Iteration: 4 bestvalit: 3.017839 bestmemit:    0.400891    3.482776    0.990162
#> Iteration: 5 bestvalit: 3.017839 bestmemit:    0.400891    3.482776    0.990162